{"id":"W2003307016","doi":"10.1016/j.spl.2008.01.056","title":"A variance component test for mixed hidden Markov models","year":2008,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Component (thermodynamics); Hidden Markov model; Variable-order Markov model; Markov model; Statistics; Covariate; Markov chain; Variance components; Variance (accounting); Random effects model; Econometrics; Test (biology); Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02652371,0.001731675,0.003501995,0.006320172,0.002272642,0.004159031,0.005733448,0.00385732,0.01154616],"category_scores_gemma":[0.1620615,0.00153814,0.002583155,0.004177542,0.003693585,0.005477788,0.003865028,0.004167198,0.00193178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055638,"about_ca_system_score_gemma":0.004682002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002782597,"about_ca_topic_score_gemma":0.002498271,"domain_scores_codex":[0.967738,0.02363487,0.001175577,0.003522879,0.00309881,0.0008300187],"domain_scores_gemma":[0.7740057,0.2070356,0.002803839,0.009052253,0.005506967,0.001595539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005390772,0.0008215577,0.02822246,0.0006678624,0.002632696,0.0008705843,0.0004679858,0.09547625,0.006108399,0.3829938,0.0133707,0.462977],"study_design_scores_gemma":[0.0005349399,0.0004426348,0.005316072,0.0001172213,0.0003978487,0.0004199648,0.0001271714,0.7313739,0.002585422,0.2553367,0.003181644,0.0001664778],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02769329,0.0002984705,0.9676807,0.0004840064,0.0001772448,0.0001873634,0.0003754336,0.001174243,0.001929187],"genre_scores_gemma":[0.4087928,0.000349192,0.5805366,0.0005204072,0.0006033571,0.001306692,0.002965653,0.0008399615,0.004085402],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02652371,"threshold_uncertainty_score":0.1402724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291929726161361,"score_gpt":0.2575266697293719,"score_spread":0.2246073724677583,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}